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REGULAR ARTICLE
Open Access
Genetic flow directionality and geographical
segregation in a Cymodocea nodosa genetic
diversity network
A Paolo Masucci1 , Sophie Arnaud-Haond2 , Víctor M Eguíluz3 , Emilio Hernández-García3* and
Ester A Serrão4
*
Correspondence:
emilio@ifisc.uib-csic.es
3
IFISC (CSIC-UIB), Instituto de Física
Interdisciplinar y Sistemas
Complejos, Consejo Superior de
Investigaciones Científicas Universitat de les Illes Balears, Palma
de Mallorca, Spain
Full list of author information is
available at the end of the article
Abstract
We analyse a large data set of genetic markers obtained from populations of
Cymodocea nodosa, a marine plant occurring from the East Mediterranean to the
Iberian-African coasts in the Atlantic Ocean. We fully develop and test a recently
introduced methodology to infer the directionality of gene flow based on the
concept of geographical segregation. Using the Jensen-Shannon divergence, we are
able to extract a directed network of gene flow describing the evolutionary patterns
of Cymodocea nodosa. In particular we recover the genetic segregation that the
marine plant underwent during its evolution. The results are confirmed by natural
evidence and are consistent with an independent cross analysis.
Introduction
With the advances of sequencing technology and the availability of large datasets, evolutionary biology needs to employ novel techniques, which are akin to those developed
within statistical physics [], to analyse and understand patterns in population dynamics.
An interesting question in evolutionary biology is how to trace the directionality in migration patterns, a problem of outstanding importance in conservation biology in general,
including the management of threatened or exploited species and of invasion processes
[]. It is also related to the more general problem of infection and information propagation in networks [, ].
In this paper we tackle the indirect assessment of migratory transfers (by pollen, propagules, or plant fragments) among plant populations using molecular markers to retrace the
exchange of genes, or gene flow, among them. This is done by fully developing a recently
introduced methodology based on a directionality index []. This index finds its origins on
the concept of geographical segregation [], often related to social studies, to infer the evolutionary pathways from microsatellite datasets. Microsatellites [] are portions of noncoding DNA with a variable number of repetitions of a motif consisting of a few bases.
They are widely used in intraspecific genetic studies. Despite being non-coding, we will
use eventually the word genes to denote the microsatellites, and alleles for their different
varieties occurring at a particular position in the genome (a particular locus).
Classical population genetics analyses do not allow inferring the direction of migration
using molecular data. Although some Bayesian analyses have been more recently devel© 2012 Masucci et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction
in any medium, provided the original work is properly cited.
Masucci et al. EPJ Data Science 2012, 1:11
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oped to do so [, ], many require complex and time-consuming computing of the likelihood functions, restraining the ability to explore more than often too simple evolutionary
scenarios and molecular models [].
In the present study we analyse a dataset that presents evident cases of geographical segregation, such as island effects and we are able to show that the proposed methodology
spots these islands, based on microsatellite data only and without any further geographical
information or evolutionary assumption. In particular we use the information contained
in microsatellite genetic markers from the entire geographic distribution of a marine plant
species, Cymodocea nodosa (CN). This dataset has been selected because there is enough
information to infer the past history of the gene flow based on the geographical distribution of genetic polymorphism [], allowing the assessment of the usefulness of the new
methods here described.
Understanding the pathways of gene flow along the Mediterranean-Atlantic transition
zone was the main aim of this genetic dataset []. Based on presence/absence of alleles,
this revealed that the flow of genetic information across the Mediterranean-Atlantic transition zone had most likely occurred westwards, because dominant Mediterranean alleles
penetrate into the nearest Atlantic sites (Atlantic Iberia), but the opposite is not true, i.e.
dominant Atlantic alleles are not found in any Mediterranean populations. This clear pattern of presence/absence of diagnostic alleles results in that this data set provides an ideal
workbench to test and to develop a recently introduced method of inferring directionality
of gene flow, here based on distances computed from the Jensen-Shannon divergence [].
Network theory has already proved to be useful in the study of metapopulation systems
dynamics []. In particular it has been shown that the analysis of topological relationships
between different populations carries fundamental information for the understanding of
evolutionary dynamics []. It is important to further develop and test methodologies to
extract reliable information-flow networks from biological datasets.
The method considered in this work introduces some novelties with respect to classical
approaches, which have been already underlined in []. The gene flow network is extracted
by means of a model-independent measure, which is a normalised version of the JensenShannon divergence. An interesting aspect of the application of this method is that we
extract the gamete space from the allele sequences and we perform the analysis in that
space. Gametes are the mother and father sexual cells that fuse in a sexual reproduction
event. The nuclear genetic information from the two gametes remain mostly separated
in the daughter cell (in the different chromosomes of each chromosome pair) and subsequent cells originating from it, but sequencing techniques cannot identify which gene is
coming from which gamete. From the observed alleles in each individual in our dataset we
construct the set of possible gametes that could have originated such individual, and we
apply our techniques to such gamete pool. The distance measure in the gamete space has
to be considered more detailed than the one in the allelic space, since it takes into account
the possible correlations between different loci. An extension of the method to include
mutation effects can be easily obtained as better explained below.
Here we advance the method beyond its first application [], in fully developing and
verifying the directionality index methodology by introducing a test for the detected directionality significance. This is done with an ad-hoc randomization test. This method is
independent of the way the genetic flow is extracted and can then be applied independently of the genetic distance used. The present analysis reveals that the methodology is
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efficient in cases of evident geographical segregation, for which the method was designed,
while it is not efficient in detecting the direction of the flows where geographical segregation is not present.
Moreover in the Additional file , we give a detailed comparison of the distance method
applied here with some of the most used genetic distance measures based on microsatellite analysis, such as the Nei distance [], the Cavalli-Sforza distance [], the Goldstein
distance [] and the average square distance [].
Data and results
The genetic flow network
Our dataset consists of  ramets of Cymodocea nodosa. A ramet is a single plant shoot,
whereas a genet is a genetic individual, or clone, derived from a single event of sexual
recombination (i.e., from a single seed) and having given birth through clonal growth to
a population of ramets therefore sharing the same genome []. Ramets were sampled
from  different meadows, distributed geographically between the Mediterranean basin
and the Atlantic Ocean, covering the entire plant distribution []. This dataset is the one
discussed in [] with the addition of a few populations more recently sampled, as for
example from Morocco. Among the  ramets taken at each site, after removal of genet
(i.e., clonal) repetitions the number of ramets available in the data set ranged from  to 
per meadow. Hence each of the  ramets of the dataset represents a different clone.
We characterize each ramet by some microsatellite markers []. In particular, each ramet
has been genotyped to identify in it n =  pairs of alleles, i.e. pairs of microsatellites that
occupy a specific position (locus) on the chromosomes, each element of the pair characterizing the same locus in the two homologous chromosomes arising from the maternal
and paternal gametes in a sexual reproduction event. The number n of pairs of microsatellites used was selected for highest information content with minimum cost (see [] and
references therein), a standard microsatellite genotyping methodology.
To characterize the presence of gene flow between meadows we use the general network strategy [, , ], in which populations (here, the meadows) are nodes of a graph,
and they are linked when significant relationships among them (indicating gene flow) are
detected.
There are different ways to implement such inference of gene flow, mostly based on
different types of distances [, , ]. Here we use a methodology based on information
theory [], which is especially suited to compare genetic data taken from populations of
different sizes, taking into account not just properties of individual alleles but also the
full information genotyped (including correlations among sites, linkage disequilibrium,
for example).
We consider the set of meadows as a metapopulation system where each population
is a meadow and each population element is an n-dimensional vector representing an
equiprobable gamete of that meadow, where n is the number of loci.
To derive the gamete pool we notice that each ramet is characterised by a set of n =
 pairs (a, b) of alleles, each pair belonging to a given locus. Since it is not known a-priori
which chromosome a given allele belongs to, we consider all the possible combinations
of alleles between the n pairs, i.e. n =  equiprobable gametes representing a single
ramet. In this way our system becomes represented by  ·  = , points in an dimensional space. Each meadow can be characterised by a discrete probability function
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–x ) assigning a relative weight to each of the possible gametes →
–x in n-dimensional
P = P(→
→
–
→
–
space, i.e. P = P( x ) is the probability to find the gamete x in that meadow.
–x ) and Q =
For each pair of meadows characterised by probability distributions P = P(→
→
–
Q( x ) we calculate the normalised Jensen-Shannon divergence
JSD(PQ) ≡
H(πA P + πB Q) – πA H(P) – πB H(Q)
,
–πA ln πA – πB ln πB
()
→
–
→
–
–x F( x ) ln F( x )
with πA,B = nA,B /(nA + nB ), ni the sample size of meadow i and H(F) = – →
the Shannon index or entropy of distribution F.
The information-theoretic meaning of JSD is discussed in detail in [, , ]. We stress
that JSD is a measure of difference between P and Q that takes into account the information on the gametes which are shared by both populations as well as the ones which
are exclusive to one of them. This second capability is not present in other informationtheoretic distances, such as the Kullback-Leibler divergence [].
In the Additional file , we illustrate in detail the relationships between Eq. () and other
classical measures to calculate genetic divergence. We want to stress that Eq. () is independent of any evolutionary assumption, i.e. it just calculates the punctual correlations
within the meadows’ attribute probability distributions. Nevertheless it is possible to relax
and extend such a measure to include mutation effects, just considering the computation
of the probability distribution in an n-dimensional sphere of a given radius, surround–x . Varying such a radius, it is then possible to coarse-grain the system at
ing the point →
different resolutions.
JSD distances are calculated among all pairs of meadows. Smaller distance implies
stronger genetic identity among the meadows. By selecting a particular threshold value
for the distance we can represent the genetic flow as a network [, ] in which meadows
with smaller JSD distance appear linked. As we increase the threshold we observe how the
different linked clusters of populations become larger and merge.
A convenient threshold to use is the percolation threshold [], at which a connected
path across the whole geographic area first appears. The network fragments for threshold
values below the percolation threshold, while the genetic flow spanning the network remains robust above it. At this point different clusters, and subclusters, representing sets
of meadows with important internal gene flow, and gene paths among them, can be identified.
In Figure  we show the network at the percolation threshold, when the major components in the network connect. As we can see the purely topological genetic flow network accurately reflects the geographical locations to which the different meadows belong. There are well connected clusters representing Senegal, Mauritania, Canaries and
Madeira, then we have the other large cluster spanning within the Mediterranean meadows. Between those two big clusters we find the Atlantic Iberian meadows and the Moroccan meadows.
While this representation is interesting to understand the effectiveness of JSD as a genetic divergence measure, it also confirms the findings of [] regarding the main evolutionary scenario for CN. To see that we plot in Figure  the connected clusters that form
when increasing the distance threshold. We interpret that the first populations to merge
when the genetic distance threshold is raised from zero are the ones that differentiated
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Figure 1 Genetic flow network for Cymodocea nodosa. Genetic flow network for Cymodocea nodosa
meadows at the percolation threshold, obtained via the JSD measure applied to raw gamete data from the
sampled meadows (the genetically very distant and disconnected Greek populations are not shown).
Different colors indicate the different meadow origins. The network is displayed via a spring embedding
algorithm, i.e. it does not contain geographical information. Nevertheless the genetic clusters well trace the
different geographical origins, as it is possible to see by comparison with the approximately overlayed map
displayed below.
more recently. Then small genetic divergences correspond to the most recent time of divergence. In our analysis we observe that the first meadows to be connected are the ones
South of the Canaries, while the ones that are more distant are the ones in Greece (those
do not appear in the figure, since they are not connected until higher distance thresholds),
with a range of intermediate distances in between. Then we can infer an evolutionary
dynamics that starts in the Greek Sea, goes to the main Mediterranean basin and then
spreads in the Atlantic. This coincides with the ancient history of habitat colonization
by this species inferred in [], which proposes that the species originated in the eastern
Mediterranean by divergence from its close relative in the Indian Ocean/Red Sea and colonized the western Mediterranean and Atlantic by spreading westwards. Moreover the
fact that Senegal and Mauritania cluster strongly with the Canaries and Madeira, in respect to the weak clustering of the Canaries with Morocco and the Iberian Atlantic, is
in agreement with the evolutionary scenario previously suggested on the basis of biogeographical information. These findings are also in agreement with the possible extinction of
meadows in Morocco and Iberia during the last glacial maximum, that was accompanied
by a drop in sea surface temperatures below the range at which CN commonly occurs,
and by a drop of sea level that changed coastline morphology [], that would have been
followed by colonization of this region by an admixture of Atlantic and Mediterranean
genetic types [].
Inferring genetic flow directionality
The second part of the analysis is about inferring the directionality of the detected gene
flows. The main question we pose is: ‘is it possible to understand which is the source and
which the sink in a given genetic channel?’. To understand this, we further develop the
technique introduced in [] based on geographical segregation []. We say that a population is segregated when it contains elements (gametes in our case, but this could equally
be applied to alleles, as shown in the Additional file ) quite exclusive and distinct from
the rest of populations, and elements common in other populations are not so abundant
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Figure 2 Genetic flow network for Cymodocea nodosa at growing values of the distance threshold.
Clusters that appear when linking populations with a JSD distance smaller than a distance threshold T , for
increasing values of T approaching from below the percolation threshold. (a) T = 0.57. (b) T = 0.89.
(c) T = 0.95. (d) T = 0.97, just below the percolation threshold. The cluster colors are the same as in Figure 1.
here. The main reasoning resides on the observation that a population which is initially
segregated will not maintain its character if it is open to receive gametes from other different ones. It will remain segregated only if there is no gene exchange or if there is some
but the population acts as a source.
In terms of frequency distributions, these are peaked on particular elements in segregated populations, whereas distributions are more unstructured in those acting as sinks
receiving genes from different sources. Between any pair of populations among which a
genetic flow has been detected (as described in the previous section, this occurs when
their genetic distance is smaller than the given percolation threshold), a directionality index IPQ is defined between meadows characterized by gamete distributions P and Q. It
takes into account the different segregation state of the pairs of populations by means of
the respective Shannon information indices, corrected for their different sizes and number
of common elements.
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To be more precise, given the two populations characterized by P and Q let us denote
by DP the set of different gametes present in the first population, DQ the ones present in
the second, X = DP ∪ DQ the total set between both populations, and J = DP ∩ DQ the set
of common gametes (that we assume to be non-empty). We denote by μP the fraction
between the number of gametes common to both populations with respect to the total
number of gametes in the first one, and analogously we define μQ as the fraction of common gametes between both populations with respect to the total number of gametes in the
second one. We have  < μP,Q ≤  and if μP or μQ is close to one, it means that the shared
–x ),
alleles are the dominant part of the corresponding population. Let us denote by PJ = PJ (→
→
–
with x ∈ J, the frequency distribution of the alleles in the first population, normalized to
unity when summing over all the common alleles (the J set), and analogously we define
–x ), with →
–x ∈ J.
QJ = QJ (→
With these definitions, the directionality index is defined as []:
H(PJ ) H(QJ )
IPQ ≡ – sign
–
,
μP
μQ
()
where H is the Shannon index of the distributions, as defined in the previous subsection.
If IPQ =  we can infer a direction of the genetic flow from the first to the second population, whereas IPQ = – indicates the reverse flow. The idea is that the net flow of gametes
is from the less entropic (i.e., less diverse, more segregated) populations towards the more
entropic, properly adjusting for the different sizes and fraction of common alleles. Very
diverse populations cannot be sources of only very specific gametes, and they are more
likely to be sinks, whereas the reverse will be true for segregated populations. Large width
of gamete distributions may be an indicator of large diversity, but it is better to use the
Shannon information as a more robust indicator of diversity. For more information about
our directionality indicator we refer to [].
Significance test of the detected directions
The idea of tracing the directionality of gene flow via a measure of geographical segregation turns out to be a delicate point. First of all this method is applicable in case there
are traces of segregation in the subset of shared gametes of the considered meadow. After
that we have to understand if the difference in the segregation indexes between the two
meadows in consideration is large enough to let us infer a direction for the genetic flow.
To address these points first we should remember that the data set used to infer directionality is not the whole set of gametes, as used for network construction, but just those
gametes that are shared by the two samples in consideration. A convenient way to quantify
the number of shared gametes is by means of the proportion μP of shared gametes between
populations characterized by distributions P and Q, with respect to the total number of
gametes in the population characterized by P. We emphasize that μP is not only a property
of population P, but of the pair of populations being compared. This proportion should
be non-negligible in order to infer reliable conclusions for the flow directionality.
Second, to understand if the detected directionality IPQ between two meadows of size n
and n is a sign of segregation or it is a random effect, we first measure the magnitude for
the directionality index IPQ and then we measure it again after having shuffled the sample.
To shuffle the sample we consider two meadows of size n and n and we fill them randomly with the genets extracted from the original two meadows. We repeat this operation
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Figure 3 Gene flow directionality network. Gene flow directionality network for Cymodocea nodosa
genetic flow network evaluated in the gamete space. Nodes represent populations of meadows grouped
together by their geographical attributes. Arrows are directional genetic flows, their width is proportional to
the value of R, quantifying the significance of the inferred directionality.
, times, so to obtain an estimation of the distribution for the randomised directionality index, which turns out to be Gaussian. From there one gets the average randomised
R
and its standard deviation σR .
directionality index IPQ
R
The ratio R = |IPQ – IPQ |/σR gives us the number of standard deviations (sd) the measured segregation index is far from the expected one in a random situation. For instance
obtaining a value of R larger than . has a probability p < . of being explained by
randomness.
Results
To have a better statistical sample, we group the elements of the clusters shown in Figure  into single populations. Since we are interested in retrieving the directionalities of
gene flow, we cluster the meadows according to their geographical origin. Nevertheless
such a clustering is not significatively different from the one that would be obtained via a
technique based, for example, on modularity minimization [].
In Figure  we show the results for the directional analysis. The widths of the arrows
are proportional to R, quantifying the significance of the relationship. The genetic flow
directionality traces are found to be significant in terms of R and μ (see Table ) from
the Spanish Coast and Sicily to the Balearic Islands, from the African coast to the Canary
Islands and Madeira, from the East (Mediterranean) Spanish Coast to the West (Mediterranean) Spanish Coast and from Morocco to the Iberian Atlantic. These results agree with
expectations from inferences based on allele presence/absence by [] and [].
These results tell us that the method correctly identifies phenomena of geographical
segregation. This phenomenon is typical for islands, where it is not easy for species to genetically mix with far away populations. Then we can see that all the islands in the studied
sample are identified as sinks of the genetic flow. Also the main east toward west spread
trend has been identified by this methodology.
In contrast with the recent events of gene flow, this method is not efficacious to trace the
directionality from the Mediterranean basin to the Atlantic, a much more ancient process
that took place before all the glacial range shifts and post-glacial recolonization migra-
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Table 1 Results of the randomization test for the directionality analysis
Link P-Q
R
μP
μQ
WA-AI
MO-AI
SI-BI
ESC-BI
SI-BI
MO-IA
ESC-WSC
WSC-BI
442.48
159.16
65.45
15.64
65.45
13.61
4.92
4.4
0.26
0.03
0.02
0.17
0.02
0.07
0.09
0.24
0.01
0.004
0.008
0.06
0.008
0.03
0.07
0.13
Only the significant (p < 0.01) directionalities are listed. In the first column the population pairs, in the second column the
value of R calculated over 1000 replicas, in the third column μP , in the fourth column μQ . The acronyms stand for: WA: West
African coast, AI: Atlantic islands, MO: Morocco, SI: Sicily, BI: Balearic Islands, ESC: East Spanish coast, IA: Iberian Atlantic, WSC:
West Spanish coast.
tions, since there is no trace of segregation in that case. Directionality of the genetic flow,
at such ancient scales, cannot be spotted with this technique.
Discussion
The directional network resulting from this analysis is in agreement with diverse and converging information on historical fluctuation of species ranges along the MediterraneanAtlantic transition zone, associated to paleoclimatic events, putative recolonization pathways and secondary contact zones inferred from biogeographic analysis on various taxa,
and expectation derived from oceanographic modeling.
The directed network (Figure ) reveals a clear trend of gene flow of South-Western
preferential migration pathways from the Western and central Mediterranean meadows
towards the Almeria-Oran front (an oceanographic feature east of the Gibraltar strait).
This coincides with the gene flow paths that were previously hypothesized on the basis of
many private alleles found in the Atlantic that do not enter this transition zone, whereas
Mediterranean polymorphism was much more shared with populations from the transition zones []. Modeled Lagrangian dispersal trajectories across the East-West Mediterranean divide []) from March to June, the fruit dispersal season for Posidonia oceanica
(which flowers in the winter), support a main trend of particle exchange toward West in
the Mediterranean []. This was associated to a lack of dispersal expected toward Sicily
and the Eastern part of the Mediterranean, as predicted from the prevailing current directions. Despite having a different reproductive season (flowering in spring, seeds produced in summer) and seeds that develop buried in the sediment and are not expected
to disperse with currents, CN is likely to disperse via reproductive plant fragments, drifting along these same current directions, in agreement with the inferences of the present
directionality flow network analysis, from Sicily westwards.
Besides this direction of the Mediterranean flow toward the Almeria-Oran populations,
a second major result with the presented methodology is the confirmed lack of input of the
Atlantic into the Mediterranean. Preferential pathways of migration within the Atlantic
show strong directionality of flow from Western Africa and Morocco toward the Canary
Islands, and from Morocco toward Iberian Atlantic populations of the transition zone,
with no sign of any entrance of Atlantic flow into the Mediterranean.
In summary, the directed network built here confirms a dominant direction of fluxes
with an East-West direction in the Mediterranean from Sicily and Spain toward South
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Western meadows and Almeria-Oran Front, and the lack of mirror exchange from the
Atlantic toward the Mediterranean.
A phenomenon that can confound some of our techniques is that of a genetic bottleneck.
This consists of a large reduction of population size, which leads also to a much decreased
diversity because of genetic drift []. This happens for example if only a few individuals
colonize a new island (founder effect) and expand there. The recovered new population
has small diversity, with the potential to be identified as a ‘source’ although in fact it has
been the receiver of the gene flow. Fortunately this is not a problem because our methodology does not compute the directionality index for all pairs of populations, but only among
those with a strong similarity, as measured by the JSD distance. Our measure of genetic
flow will give a very small value in the case mentioned above. In addition, even if a recent and strong bottleneck (i.e. contemporary to the sampling) or a recent re-expansion
post-bottleneck may transitorily induce ‘source-like’ characteristics in the distributions,
such distributions would display a characteristic lack of polymorphism in the first case,
and a typical genetic signature on the distribution of polymorphism and divergence in the
latter that will be easily detected by available population genetics tools [, ]. A careful
examination of data should thus allow discarding such confounding factor or pointing it
as a possible alternative explanation of directionally detected with our methods.
We stress that the data treatment presented in this paper is independent of evolutionary
assumptions, but that it can be easily extended to include mutation. This can be done, as
we already point out, coarse-graining the probability space in function of a simple parameter indicating the resolution with which we distinguish different gametes. Then varying
this parameter it is possible to study the evolutionary paths at different time windows, but
further research in this direction is needed to better implement these points.
While the present research is about genetic flow dynamics, the whole information
flow/directionality index method has a wider range of application, from metapopulation
systems to social dynamics and more in general it can be relevant whenever a given population can be represented by vectors of attributes in a symbolic space.
A set of Matlab routines implementing the network analysis described here can be found
in [].
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
APM developed the methodology and performed the analyses. SAH and EAS provided the data and the biological
interpretations. VME and EHG contributed to the methods and to the analyses. All authors participated in the writing of
the paper.
Author details
1
Centre for Advanced Spatial Analysis, University College of London, London, UK. 2 Département Étude des Ecosystèmes
Profonds-DEEP, Laboratoire Environnement Profond-LEP, Institut Français de Recherche pour l’Exploitation de la MER,
Centre de Brest, France. 3 IFISC (CSIC-UIB), Instituto de Física Interdisciplinar y Sistemas Complejos, Consejo Superior de
Investigaciones Científicas - Universitat de les Illes Balears, Palma de Mallorca, Spain. 4 CCMAR, CIMAR-Laboratório
Associado, Universidade do Algarve, Gambelas, Faro, 8005-139, Portugal.
Acknowledgements
We thank Filipe Alberto for supplying the data, and discussing the results. Supported by Ministerio de Economía y
Competitividad (Spain) and Fondo Europeo de Desarrollo Regional through projects FISICOS (FIS2007-60327) and
MODASS (FIS2011-24785). SAH acknowledges support from the ANR Clonix project (ANR-11-BSV7-007). Publication fee
has been covered partially by the CSIC Open Access Publication Support Initiative through its Unit of Information
Resources for Research (URICI).
Received: 18 June 2012 Accepted: 14 November 2012 Published: 28 November 2012
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Masucci et al. EPJ Data Science 2012, 1:11
http://www.epjdatascience.com/content/1/1/11
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doi:10.1140/epjds11
Cite this article as: Masucci et al.: Genetic flow directionality and geographical segregation in a Cymodocea nodosa
genetic diversity network. EPJ Data Science 2012 1:11.
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